Explainable NLP and rationale
Explaining the rationale of the result of an AI model is necessary for it to be explainable to humans [1]}. Some Natural Language Processing (NLP) studies define rationale as snippets of an input text on which the model's prediction is supported [2]}, [3]}. [3]} implemented a generator that generates words considered rationales and used them as input of an encoder for sentiment classification. [5]} mapped the human rationales into the model attention values to solve the low-resource problem by learning a domain-invariant token representation. For hate speech detection, HateXplain employs the human rationales as ground truth attention to concentrate on aggressive tokens. Unlike existing approaches, we utilize the masked human rationale label embeddings as input. They become the useful additional information of each token.
